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PRESTO: A Processing-in-Memory-Based k-SAT Solver Using Recurrent Stochastic Neural Network With Unsupervised Learning | IEEE Journals & Magazine | IEEE Xplore

PRESTO: A Processing-in-Memory-Based k-SAT Solver Using Recurrent Stochastic Neural Network With Unsupervised Learning


Abstract:

In this article, we introduce a processing-in-memory (PIM)-based satisfiability (SAT) solver [called Processing-in-memory-based SAT solver using a Recurrent Stochastic ne...Show More

Abstract:

In this article, we introduce a processing-in-memory (PIM)-based satisfiability (SAT) solver [called Processing-in-memory-based SAT solver using a Recurrent Stochastic neural network (PRESTO)], a mixed-signal circuit-based PIM (MSC-PIM) architecture combined with a digital finite state machine (FSM) for solving SAT problems. The presented design leverages a stochastic neural network with unsupervised learning. PRESTO’s architecture supports fully connected k -SAT clauses with mixed- k problems, highlighting its versatility in handling a wide range of SAT challenges. A test chip is fabricated in 65-nm CMOS technology with a core size of 0.4 mm2 and demonstrates an operating frequency range of 100–500 MHz and a peak power of 35.4 mW. The measurement results show that PRESTO achieves a 74.0% accuracy for three-SAT problems with 30 variables and 126 clauses.
Published in: IEEE Journal of Solid-State Circuits ( Volume: 59, Issue: 7, July 2024)
Page(s): 2310 - 2320
Date of Publication: 26 January 2024

ISSN Information:


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